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首页> 外文期刊>Geoscientific Model Development >Evaluating a fire smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple observation data sets during the Southeast Nexus (SENEX) field campaign
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Evaluating a fire smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple observation data sets during the Southeast Nexus (SENEX) field campaign

机译：通过在南部Nexus（Semex）田野活动期间使用多种观察数据集来评估国家空气质量预测能力（NAQFC）的火灾烟雾仿真算法

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Multiple observation data sets – Interagency Monitoring of Protected Visual Environments (IMPROVE) network data, the Automated Smoke Detection and Tracking Algorithm (ASDTA), Hazard Mapping System (HMS) smoke plume shapefiles and aircraft acetonitrile (CH3CN) measurements from the NOAA Southeast Nexus (SENEX) field campaign – are used to evaluate the HMS–BlueSky–SMOKE (Sparse Matrix Operator Kernel Emission)–CMAQ (Community Multi-scale Air Quality Model) fire emissions and smoke plume prediction system. A similar configuration is used in the US National Air Quality Forecasting Capability (NAQFC). The system was found to capture most of the observed fire signals. Usage of HMS-detected fire hotspots and smoke plume information was valuable for deriving both fire emissions and forecast evaluation. This study also identified that the operational NAQFC did not include fire contributions through lateral boundary conditions, resulting in significant simulation uncertainties. In this study we focused both on system evaluation and evaluation methods. We discussed how to use observational data correctly to retrieve fire signals and synergistically use multiple data sets. We also addressed the limitations of each of the observation data sets and evaluation methods.
机译：多种观察数据集 - 受保护的视觉环境的间隙监控（改进）网络数据，自动烟雾检测和跟踪算法（ASDTA），危险映射系统（HMS）烟雾羽毛分层和飞机乙腈（CH3CN）测量来自NOAA东南Nexus（ SENEX）现场运动 - 用于评估HMS-BLUESKY-SMOWS（稀疏矩阵操作员核发射）-CMAQ（社区多尺度空气质量模型）消防和烟雾羽流预测系统。在美国国家空气质量预测能力（NAQFC）中使用了类似的配置。发现该系统捕获大多数观察到的火信号。用于检测HMS检测到的火热热点和烟雾羽流信息的使用对于导出消防排放和预测评估是有价值的。本研究还确定，运营NAQFC通过横向边界条件不包括火灾贡献，导致显着的模拟不确定因素。在这项研究中，我们都集中在系统评估和评估方法上。我们讨论了如何正确使用观测数据来检索火灾信号并协同使用多个数据集。我们还解决了每个观察数据集和评估方法的局限性。

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